{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5hIbr52I7Z7U"
   },
   "source": [
    "Deep Learning\n",
    "=============\n",
    "\n",
    "Assignment 1\n",
    "------------\n",
    "\n",
    "The objective of this assignment is to learn about simple data curation practices, and familiarize you with some of the data we'll be reusing later.\n",
    "\n",
    "This notebook uses the [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) dataset to be used with python experiments. This dataset is designed to look like the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset, while looking a little more like real data: it's a harder task, and the data is a lot less 'clean' than MNIST."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": false,
    "id": "apJbCsBHl-2A"
   },
   "outputs": [],
   "source": [
    "# These are all the modules we'll be using later. Make sure you can import them\n",
    "# before proceeding further.\n",
    "from __future__ import print_function\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import tarfile\n",
    "from IPython.display import display, Image\n",
    "from scipy import ndimage\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from six.moves.urllib.request import urlretrieve\n",
    "from six.moves import cPickle as pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jNWGtZaXn-5j"
   },
   "source": [
    "First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The labels are limited to 'A' through 'J' (10 classes). The training set has about 500k and the testset 19.000 labelled examples. Given these sizes, it should be possible to train models quickly on any machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 186058,
     "status": "ok",
     "timestamp": 1444485672507,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "EYRJ4ICW6-da",
    "outputId": "0d0f85df-155f-4a89-8e7e-ee32df36ec8d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found and verified notMNIST_large.tar.gz\n",
      "Found and verified notMNIST_small.tar.gz\n"
     ]
    }
   ],
   "source": [
    "url = 'http://yaroslavvb.com/upload/notMNIST/'\n",
    "\n",
    "def maybe_download(filename, expected_bytes, force=False):\n",
    "  \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n",
    "  if force or not os.path.exists(filename):\n",
    "    filename, _ = urlretrieve(url + filename, filename)\n",
    "  statinfo = os.stat(filename)\n",
    "  if statinfo.st_size == expected_bytes:\n",
    "    print('Found and verified', filename)\n",
    "  else:\n",
    "    raise Exception(\n",
    "      'Failed to verify' + filename + '. Can you get to it with a browser?')\n",
    "  return filename\n",
    "\n",
    "train_filename = maybe_download('notMNIST_large.tar.gz', 247336696)\n",
    "test_filename = maybe_download('notMNIST_small.tar.gz', 8458043)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cC3p0oEyF8QT"
   },
   "source": [
    "Extract the dataset from the compressed .tar.gz file.\n",
    "This should give you a set of directories, labelled A through J."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 186055,
     "status": "ok",
     "timestamp": 1444485672525,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "H8CBE-WZ8nmj",
    "outputId": "ef6c790c-2513-4b09-962e-27c79390c762"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large already present - Skipping extraction of notMNIST_large.tar.gz.\n",
      "['notMNIST_large/A', 'notMNIST_large/B', 'notMNIST_large/C', 'notMNIST_large/D', 'notMNIST_large/E', 'notMNIST_large/F', 'notMNIST_large/G', 'notMNIST_large/H', 'notMNIST_large/I', 'notMNIST_large/J']\n",
      "notMNIST_small already present - Skipping extraction of notMNIST_small.tar.gz.\n",
      "['notMNIST_small/A', 'notMNIST_small/B', 'notMNIST_small/C', 'notMNIST_small/D', 'notMNIST_small/E', 'notMNIST_small/F', 'notMNIST_small/G', 'notMNIST_small/H', 'notMNIST_small/I', 'notMNIST_small/J']\n"
     ]
    }
   ],
   "source": [
    "num_classes = 10\n",
    "np.random.seed(133)\n",
    "\n",
    "def maybe_extract(filename, force=False):\n",
    "  root = os.path.splitext(os.path.splitext(filename)[0])[0]  # remove .tar.gz\n",
    "  if os.path.isdir(root) and not force:\n",
    "    # You may override by setting force=True.\n",
    "    print('%s already present - Skipping extraction of %s.' % (root, filename))\n",
    "  else:\n",
    "    print('Extracting data for %s. This may take a while. Please wait.' % root)\n",
    "    tar = tarfile.open(filename)\n",
    "    sys.stdout.flush()\n",
    "    tar.extractall()\n",
    "    tar.close()\n",
    "  data_folders = [\n",
    "    os.path.join(root, d) for d in sorted(os.listdir(root))\n",
    "    if os.path.isdir(os.path.join(root, d))]\n",
    "  if len(data_folders) != num_classes:\n",
    "    raise Exception(\n",
    "      'Expected %d folders, one per class. Found %d instead.' % (\n",
    "        num_classes, len(data_folders)))\n",
    "  print(data_folders)\n",
    "  return data_folders\n",
    "  \n",
    "train_folders = maybe_extract(train_filename)\n",
    "test_folders = maybe_extract(test_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4riXK3IoHgx6"
   },
   "source": [
    "---\n",
    "Problem 1\n",
    "---------\n",
    "\n",
    "Let's take a peek at some of the data to make sure it looks sensible. Each exemplar should be an image of a character A through J rendered in a different font. Display a sample of the images that we just downloaded. Hint: you can use the package IPython.display.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, let's import some libraries that I will use later on and activate online display of matplotlib outputs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import hashlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def disp_samples(data_folders, sample_size):\n",
    "  for folder in data_folders:\n",
    "    print(folder)\n",
    "    image_files = os.listdir(folder)\n",
    "    image_sample = random.sample(image_files, sample_size)\n",
    "    for image in image_sample:\n",
    "      image_file = os.path.join(folder, image)\n",
    "      i = Image(filename=image_file)\n",
    "      display(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/A\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAB50lEQVR4nEWST0vVQRSGnzMzpoZg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\nV7oITOcq6O8G8B/dVSx2rw6c7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/B\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAB0ElEQVR4nGWTPWuUQRSFn7mzamIT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\nSUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/C\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABtElEQVR4nG2QT0hUURjFz3fvHQ21\nRVmI5J9ErDa2MIJAEkExt6FIYGgJTYGGLoSQaNMucGM07RSDwJXQQlAIly7FQHE3UxBSWjOtRJ13\n7z0tem98z+asLvf3nY/zHSApBWiBoDl7WHhikkhcd8PHqqG+S9ebbNXdBNMKI1x49o2epGc2btOo\n/0BbJHf36Oj4PmaDjP5k0fLrI7yh9Ty4GmNtq3QnPJqtBdoDWi7F2PghgyLX26EqMETr2R0xg2m6\nwDKjYZSgNUfOQ/9jKTyntY4voTUAhZu/8o1QoS9NZ3nyGEbCj8xrmLCVETrLQn/EAD1xGQIAgjHr\nLb/fwmld+mLpOUnHo07Eq5SoaLTA4sVGyuI/adxnwIUwW1IKtVnySw2kDNR4R3d8O7r4jO6xyLcw\nZVnlJvm7uexSYIABX50uFTGxZIvkn2tRVC0AUFGCeXInXKoVzrXUPP3UGc2arR5sQwjR3qG3o3K0\ndWpDfOicINNIGQVB1yoPmLsTq6NtnzPQQN2DFUtyrjpx8WcyfWFsuUDP/FoPVKLGG5kcj+n5Y+nh\nlShvSYLzg2v7y8N1AFSyxL94jbv/6dh2mgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/D\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAA80lEQVR4nOWSIU4EQRBFX3XPCLIJ\nAgICgcLtEbAkGO6AIUFxDgweDoDBLhLLHZaAQ5GQYDfp6a6PYGbp2ZC5AF916vVLdaULVXG9nmDR\nGKJxurs9YhjBklJKKXXuRR/nxiCPPM+eXU9zQgQwgYfl7cyxvHOxXwzF1c31KspBUqcFDWAcPkhS\nzno5xaIhKeuRrbZt28Y4e5PLs3R/QKxNCJHdT7lUsr4uwxhiIb6rSFJ2Pa9H6t/u9FNE+XHDRqT+\nEChhE1aJU5D/An/+0WiHxajWiwYQost9wTWGgaOrmfdm2GbtWnXxz57gpar8Np00J0f5BrYDpB2k\njluNAAAAAElFTkSuQmCC\n",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/E\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAoklEQVR4nM2SMQrCQBBF32wGBStv\n4TksvYfn8hReRcRCsLEXMY3J5lskVXaiIAhO+3Yes3/GBGRRlKrHxgGqkiGbzxy6tDsuugKm5xmp\n0TrshUHrFrDsAJk2R50p9P0WOoBh5bQaoFAQIJiQnW4+gkrN9pAcjFXptHYJDl3aX+ZjrbVX9GW2\nWfxdtp+DnzgTvf2KaeI0wWoLd9VX3WujF6ruL/WvQlpNpx+0AAAAAElFTkSuQmCC\n",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/F\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAmUlEQVR4nNWSMQ7CMAxFv9MMSLD2\nNByp92g5GOrMDdi6IyEaqY3N5sSJ0oGNP+XrKc7Pl0nQUOyursUA4BD63Eg0Yy2k3JGxQst4ZrV8\nekJUUR52UOGsjQVMYq5u7ntjDqEfNJC7zyRZIKsbXLME/y7jUZfOrvoKHcAQlPq1hNN00foC2MLP\n9uLc20Cg9KpUDYnZmt/X5L/gF3lWSgMYPI+lAAAAAElFTkSuQmCC\n",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/G\n"
     ]
    },
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/H\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAB10lEQVR4nEWTsUvVYRSGn3Pu1bSi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\n4Gv/AAAAAElFTkSuQmCC\n",
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/I\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABBUlEQVR4nH2RMUvEQBCFv9mdmOMO\nRfEKEeEEC0sbC//H/ST/mZXNlRaCpSJRURFzJtlsxiJccccmA1s9vjdv3gocHB2eLM6O5/PZbJrn\nubcursufx4cnZMnVzfXUe2FrrA0tuuR0se+tQ0DoH4Z5Dxpoo+G2OYG2bdE91MuOJ0AXGtSzu66f\nGGpUcS6hWWwqVIfIjZhSY+jFpG3XVKjHjQUa35lOG0YCdSN39rYDDVkMNZqld/Zkhg7U94dmgw3V\ng+TGdqC+UTKMfVlPulTzFqs1mpEIa9ZJNkE9mCFgYIAhAs6LRSQi7KLhrXi9v1uht5xfXkzUx9BU\nZfn7+fVdFB8vz+8A/3xGfcuN5GAHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/J\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABdUlEQVR4nHWQvUtCYRTGn/Pey71a\npJallS2hUEQgLhkFtTe1tbTUEv4RLRK1ttXUGE3RHERDk4tI0McUNBShkuJHV9F7T4P341Xx2c77\nO8/D8x6Cq7VUoVSrKBYHQnq30Z40P8kmwlrNjRlf5aIKBKP+VtWYphPVhoSECn8iAUmbjpOYtpcm\nQvOxxnrk9k0PaYSPKwzIhyxHnEH13hUCcYdesFhRTAaoK0Gzt9zEeMcyey0HYpnriDvDEERjNASa\nmAGPgiYCo52ShqEGS4KC+mDQgyrIkhFB99IEcVyTIKMK3YV8lD+AIsGy5V5NxLOBdF9smAwXhhRu\nOZ8GQFimmgvfizQFdgsLRPELexTNB2yFPScjhrLX9tqY23UbCSDJFa+CnrdymqoAAKlA6o/33F0V\nl8zHgCAiwLdTYDMlBa00rfZZEgBmM4/MfKF4BxfIMHP56f4u98PMtXMN0rEF9l/ZlnGzgX4JLBxe\nP5fq5vdpGlA83z9z2oJ0vyRKlgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_samples(train_folders, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/A\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/B\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABs0lEQVR4nHWRPWhUQRhFzzczbzca\nCxNZSVZEQdef1P6BFkoWBAvB2kZECRIEEUQbbS2FEJFUYmkXFURFSCOojbWJ/4ooGzBEssnuvpn5\nLN5b2CXPW87hcpnzyakLq4agAJC2Wos/vs83wAaAa+q1P/7ny4khBHCeAPcXEgWkvLG6e2xTdXT8\n5uQjE+Gqeo1HEPLIjht/tKPxLCaDOk7JOeecs1YMh5c11UYVMQAEfJYQFPf2DiZWTndhb2LkKcAh\nWA+BVYDh/8AtqPC7AIoxnEEiL8ABYHGxuyi+fom0/OY56gB0hTQXiNt1frIUy+/PNU10AHLlc6IA\nyYZKbV/JL32bvdeQ3FB/1hZmjoKR7uaDD3lzoLJzz0itdvHT3Zk10XVuGTr5RDXoq61Iv1trrSBc\nVt/ROZtvHu/5rzjLtHZSnSgwpF6YbSfKiUJ9kS8NYPvmQrc0O4AUnAxgeBClsVQEEw6OBGE+l2C6\n24Io7W3X0eTvw/yxTcwSgg+x/my/F6bemay5d3Egu0p5cHSsfiAJTm/fIubi82JUVdWoK4+PYcEF\nWrbHbLu5/Ovr67mPmAj/AIWD8kUI0HLkAAAAAElFTkSuQmCC\n",
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       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/C\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/D\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAACI0lEQVR4nE2SS0iUURiGn/OdMy0a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\nE56+z+ue6JLDoF4o+w8ix9bNNTWFFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/E\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAB/UlEQVR4nG2SS0iUYRiFn/f9v3Eu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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/F\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABxElEQVR4nHWSP2yNURjGf+97zm39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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/G\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAACD0lEQVR4nG2SzUuUURTGf+eey+AH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\nB3EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/H\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAA30lEQVR4nNWRMU7DQBBF3+yOEuGs\nULIHoKFJQ88JuAEX4CjcgZqSnhLOwEWQYieOhYnw7lAkMdjUSPC7P3++5usPtrMrPGOU2a5xP8bf\n8L9EHVJBECAJOBRwqBhgmGH7LYOMApkuHZzTWQghzBeLE+H2UhHOL87CPMYY42lRFLPpwbtciUHn\nZXA4G6aYPCug5DzK5QAmCiQDRm5M2lexnrbv7a4p11VVrjfVQ+Fu7pXsHl/eVpu6qbdNfUzNh9Cg\ndJO7J98PZV9DEhAU8HixYwdfJRgKJFLvHODPPfuXxE+TbVAQRQ+gDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/I\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAyElEQVR4nM1TMQ7CMBDzXSsxQBES\nQ//ACxjZkXgGU/kcEv9g4AeIhYFCoQyExgwlJbQqA2LAU2JffM4pQZLyCWuMMcZYt08TySK04SyF\nCACAsl31CD1NRlZLhqhMeZ1DA0wPdMa2EllwHYQdLHmvqNDrEajhW1P11kIA0ibW42qd+ErkJxEK\naQlEHG9C7P3q14R2s34cD8YbFo4T14iSpZGFXrpDOmtppPDgj6+s8xJ9PPmbCf2HaFsvSqt543E4\nSI5F9R3qSJMHUY+dZiyIUXIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/J\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABBUlEQVR4nO2SMUsDURCEZ/ddiJIq\niIUEhFgaUgkW/obYC/4Bf0oK/4e9jY02op3dgUq0OI1goV0OjbnbHYuc5x0YC2une3wzu4/3RoId\n7+bu05eMTiNVVFVXWtoIsQh7681WZ2cQUIpydvqUTl+Lo2KfmRX64BACABGgAoTZFVTLaIylGcAI\ncACcW78kyAxA6WYVFl7FL/qHf4ECnz/1T1DxvjBJxdtiKOMEXoPfn204HwWvrdmikSQ9Y9Kt9UIa\nOKCR9Nx42y/naYgiFazd0SwzMj1so9JSAMuDG+Yk+TjcqLRQ+pvtbm97FcBkdHFymSJ42TU52nOZ\nPD8k99fxmJDglXt+Ai4Fdp1ZqBbiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_samples(test_folders, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PBdkjESPK8tw"
   },
   "source": [
    "Now let's load the data in a more manageable format. Since, depending on your computer setup you might not be able to fit it all in memory, we'll load each class into a separate dataset, store them on disk and curate them independently. Later we'll merge them into a single dataset of manageable size.\n",
    "\n",
    "We'll convert the entire dataset into a 3D array (image index, x, y) of floating point values, normalized to have approximately zero mean and standard deviation ~0.5 to make training easier down the road. \n",
    "\n",
    "A few images might not be readable, we'll just skip them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 30
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 399874,
     "status": "ok",
     "timestamp": 1444485886378,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "h7q0XhG3MJdf",
    "outputId": "92c391bb-86ff-431d-9ada-315568a19e59"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/A.pickle already present - Skipping pickling.\n",
      "notMNIST_large/B.pickle already present - Skipping pickling.\n",
      "notMNIST_large/C.pickle already present - Skipping pickling.\n",
      "notMNIST_large/D.pickle already present - Skipping pickling.\n",
      "notMNIST_large/E.pickle already present - Skipping pickling.\n",
      "notMNIST_large/F.pickle already present - Skipping pickling.\n",
      "notMNIST_large/G.pickle already present - Skipping pickling.\n",
      "notMNIST_large/H.pickle already present - Skipping pickling.\n",
      "notMNIST_large/I.pickle already present - Skipping pickling.\n",
      "notMNIST_large/J.pickle already present - Skipping pickling.\n",
      "notMNIST_small/A.pickle already present - Skipping pickling.\n",
      "notMNIST_small/B.pickle already present - Skipping pickling.\n",
      "notMNIST_small/C.pickle already present - Skipping pickling.\n",
      "notMNIST_small/D.pickle already present - Skipping pickling.\n",
      "notMNIST_small/E.pickle already present - Skipping pickling.\n",
      "notMNIST_small/F.pickle already present - Skipping pickling.\n",
      "notMNIST_small/G.pickle already present - Skipping pickling.\n",
      "notMNIST_small/H.pickle already present - Skipping pickling.\n",
      "notMNIST_small/I.pickle already present - Skipping pickling.\n",
      "notMNIST_small/J.pickle already present - Skipping pickling.\n"
     ]
    }
   ],
   "source": [
    "image_size = 28  # Pixel width and height.\n",
    "pixel_depth = 255.0  # Number of levels per pixel.\n",
    "\n",
    "def load_letter(folder, min_num_images):\n",
    "  \"\"\"Load the data for a single letter label.\"\"\"\n",
    "  image_files = os.listdir(folder)\n",
    "  dataset = np.ndarray(shape=(len(image_files), image_size, image_size),\n",
    "                         dtype=np.float32)\n",
    "  image_index = 0\n",
    "  print(folder)\n",
    "  for image in os.listdir(folder):\n",
    "    image_file = os.path.join(folder, image)\n",
    "    try:\n",
    "      image_data = (ndimage.imread(image_file).astype(float) - \n",
    "                    pixel_depth / 2) / pixel_depth\n",
    "      if image_data.shape != (image_size, image_size):\n",
    "        raise Exception('Unexpected image shape: %s' % str(image_data.shape))\n",
    "      dataset[image_index, :, :] = image_data\n",
    "      image_index += 1\n",
    "    except IOError as e:\n",
    "      print('Could not read:', image_file, ':', e, '- it\\'s ok, skipping.')\n",
    "    \n",
    "  num_images = image_index\n",
    "  dataset = dataset[0:num_images, :, :]\n",
    "  if num_images < min_num_images:\n",
    "    raise Exception('Many fewer images than expected: %d < %d' %\n",
    "                    (num_images, min_num_images))\n",
    "    \n",
    "  print('Full dataset tensor:', dataset.shape)\n",
    "  print('Mean:', np.mean(dataset))\n",
    "  print('Standard deviation:', np.std(dataset))\n",
    "  return dataset\n",
    "        \n",
    "def maybe_pickle(data_folders, min_num_images_per_class, force=False):\n",
    "  dataset_names = []\n",
    "  for folder in data_folders:\n",
    "    set_filename = folder + '.pickle'\n",
    "    dataset_names.append(set_filename)\n",
    "    if os.path.exists(set_filename) and not force:\n",
    "      # You may override by setting force=True.\n",
    "      print('%s already present - Skipping pickling.' % set_filename)\n",
    "    else:\n",
    "      print('Pickling %s.' % set_filename)\n",
    "      dataset = load_letter(folder, min_num_images_per_class)\n",
    "      try:\n",
    "        with open(set_filename, 'wb') as f:\n",
    "          pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)\n",
    "      except Exception as e:\n",
    "        print('Unable to save data to', set_filename, ':', e)\n",
    "  \n",
    "  return dataset_names\n",
    "\n",
    "train_datasets = maybe_pickle(train_folders, 45000)\n",
    "test_datasets = maybe_pickle(test_folders, 1800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vUdbskYE2d87"
   },
   "source": [
    "---\n",
    "Problem 2\n",
    "---------\n",
    "\n",
    "Let's verify that the data still looks good. Displaying a sample of the labels and images from the ndarray. Hint: you can use matplotlib.pyplot.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def disp_8_img(imgs, titles):\n",
    "  \"\"\"Display subplot with 8 images or less\"\"\"\n",
    "  for i, img in enumerate(imgs):\n",
    "    plt.subplot(2, 4, i+1)\n",
    "    plt.title(titles[i])\n",
    "    plt.axis('off')\n",
    "    plt.imshow(img)\n",
    "\n",
    "def disp_sample_pickles(data_folders):\n",
    "  folder = random.sample(data_folders, 1)\n",
    "  pickle_filename = ''.join(folder) + '.pickle'\n",
    "  try:\n",
    "    with open(pickle_filename, 'rb') as f:\n",
    "      dataset = pickle.load(f)\n",
    "  except Exception as e:\n",
    "    print('Unable to read data from', pickle_filename, ':', e)\n",
    "    return\n",
    "  # display\n",
    "  plt.suptitle(''.join(folder)[-1])\n",
    "  for i, img in enumerate(random.sample(list(dataset), 8)):\n",
    "    plt.subplot(2, 4, i+1)\n",
    "    plt.axis('off')\n",
    "    plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rNgImzsHMZXDiKe4snOP+/FnazjjoOdAzO3cIvHZ4XjSwR+Y0jF9XGD0PiaUHxJcfcObR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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104f4de90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_sample_pickles(train_folders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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10jLZ2ih0ytApUUyM88XCe8TeXaBa2qfyZA+ofs1b9OGReygE6csiI8sipgXl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pEyuD48dsBOahTOjWF/OA91Db6uyvKmx0K6bwIMYAKYxG3sYG4maCfAbkBgnzTfeHEEtoZdStF+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112820ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_sample_pickles(test_folders)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cYznx5jUwzoO"
   },
   "source": [
    "---\n",
    "Problem 3\n",
    "---------\n",
    "Another check: we expect the data to be balanced across classes. Verify that.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data is balanced across classes if the classes have about the same number of items. Let's check the number of images by class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of images in  notMNIST_large/A  :  52909\n",
      "Number of images in  notMNIST_large/B  :  52911\n",
      "Number of images in  notMNIST_large/C  :  52912\n",
      "Number of images in  notMNIST_large/D  :  52911\n",
      "Number of images in  notMNIST_large/E  :  52912\n",
      "Number of images in  notMNIST_large/F  :  52912\n",
      "Number of images in  notMNIST_large/G  :  52912\n",
      "Number of images in  notMNIST_large/H  :  52912\n",
      "Number of images in  notMNIST_large/I  :  52912\n",
      "Number of images in  notMNIST_large/J  :  52911\n",
      "Number of images in  notMNIST_small/A  :  1872\n",
      "Number of images in  notMNIST_small/B  :  1873\n",
      "Number of images in  notMNIST_small/C  :  1873\n",
      "Number of images in  notMNIST_small/D  :  1873\n",
      "Number of images in  notMNIST_small/E  :  1873\n",
      "Number of images in  notMNIST_small/F  :  1872\n",
      "Number of images in  notMNIST_small/G  :  1872\n",
      "Number of images in  notMNIST_small/H  :  1872\n",
      "Number of images in  notMNIST_small/I  :  1872\n",
      "Number of images in  notMNIST_small/J  :  1872\n"
     ]
    }
   ],
   "source": [
    "def disp_number_images(data_folders):\n",
    "  for folder in data_folders:\n",
    "    pickle_filename = ''.join(folder) + '.pickle'\n",
    "    try:\n",
    "      with open(pickle_filename, 'rb') as f:\n",
    "        dataset = pickle.load(f)\n",
    "    except Exception as e:\n",
    "      print('Unable to read data from', pickle_filename, ':', e)\n",
    "      return\n",
    "    print('Number of images in ', folder, ' : ', len(dataset))\n",
    "    \n",
    "disp_number_images(train_folders)\n",
    "disp_number_images(test_folders) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are only minor gaps, so the classes are well balanced."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LA7M7K22ynCt"
   },
   "source": [
    "Merge and prune the training data as needed. Depending on your computer setup, you might not be able to fit it all in memory, and you can tune `train_size` as needed. The labels will be stored into a separate array of integers 0 through 9.\n",
    "\n",
    "Also create a validation dataset for hyperparameter tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 411281,
     "status": "ok",
     "timestamp": 1444485897869,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "s3mWgZLpyuzq",
    "outputId": "8af66da6-902d-4719-bedc-7c9fb7ae7948"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: (200000, 28, 28) (200000,)\n",
      "Validation: (10000, 28, 28) (10000,)\n",
      "Testing: (10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "def make_arrays(nb_rows, img_size):\n",
    "  if nb_rows:\n",
    "    dataset = np.ndarray((nb_rows, img_size, img_size), dtype=np.float32)\n",
    "    labels = np.ndarray(nb_rows, dtype=np.int32)\n",
    "  else:\n",
    "    dataset, labels = None, None\n",
    "  return dataset, labels\n",
    "\n",
    "def merge_datasets(pickle_files, train_size, valid_size=0):\n",
    "  num_classes = len(pickle_files)\n",
    "  valid_dataset, valid_labels = make_arrays(valid_size, image_size)\n",
    "  train_dataset, train_labels = make_arrays(train_size, image_size)\n",
    "  vsize_per_class = valid_size // num_classes\n",
    "  tsize_per_class = train_size // num_classes\n",
    "    \n",
    "  start_v, start_t = 0, 0\n",
    "  end_v, end_t = vsize_per_class, tsize_per_class\n",
    "  end_l = vsize_per_class+tsize_per_class\n",
    "  for label, pickle_file in enumerate(pickle_files):       \n",
    "    try:\n",
    "      with open(pickle_file, 'rb') as f:\n",
    "        letter_set = pickle.load(f)\n",
    "        # let's shuffle the letters to have random validation and training set\n",
    "        np.random.shuffle(letter_set)\n",
    "        if valid_dataset is not None:\n",
    "          valid_letter = letter_set[:vsize_per_class, :, :]\n",
    "          valid_dataset[start_v:end_v, :, :] = valid_letter\n",
    "          valid_labels[start_v:end_v] = label\n",
    "          start_v += vsize_per_class\n",
    "          end_v += vsize_per_class\n",
    "                    \n",
    "        train_letter = letter_set[vsize_per_class:end_l, :, :]\n",
    "        train_dataset[start_t:end_t, :, :] = train_letter\n",
    "        train_labels[start_t:end_t] = label\n",
    "        start_t += tsize_per_class\n",
    "        end_t += tsize_per_class\n",
    "    except Exception as e:\n",
    "      print('Unable to process data from', pickle_file, ':', e)\n",
    "      raise\n",
    "    \n",
    "  return valid_dataset, valid_labels, train_dataset, train_labels\n",
    "            \n",
    "            \n",
    "train_size = 200000\n",
    "valid_size = 10000\n",
    "test_size = 10000\n",
    "\n",
    "valid_dataset, valid_labels, train_dataset, train_labels = merge_datasets(\n",
    "  train_datasets, train_size, valid_size)\n",
    "_, _, test_dataset, test_labels = merge_datasets(test_datasets, test_size)\n",
    "\n",
    "print('Training:', train_dataset.shape, train_labels.shape)\n",
    "print('Validation:', valid_dataset.shape, valid_labels.shape)\n",
    "print('Testing:', test_dataset.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GPTCnjIcyuKN"
   },
   "source": [
    "Next, we'll randomize the data. It's important to have the labels well shuffled for the training and test distributions to match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "6WZ2l2tN2zOL"
   },
   "outputs": [],
   "source": [
    "def randomize(dataset, labels):\n",
    "  permutation = np.random.permutation(labels.shape[0])\n",
    "  shuffled_dataset = dataset[permutation,:,:]\n",
    "  shuffled_labels = labels[permutation]\n",
    "  return shuffled_dataset, shuffled_labels\n",
    "train_dataset, train_labels = randomize(train_dataset, train_labels)\n",
    "test_dataset, test_labels = randomize(test_dataset, test_labels)\n",
    "valid_dataset, valid_labels = randomize(valid_dataset, valid_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "puDUTe6t6USl"
   },
   "source": [
    "---\n",
    "Problem 4\n",
    "---------\n",
    "Convince yourself that the data is still good after shuffling!\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be sure that the data are still fine after the merger and the randomization, I will select one item and display the image alongside the label. Note: 0 = A, 1 = B, 2 = C, 3 = D, 4 = E, 5 = F, 6 = G, 7 = H, 8 = I, 9 = J. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pretty_labels = {0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F', 6: 'G', 7: 'H', 8: 'I', 9: 'J'}\n",
    "\n",
    "def disp_sample_dataset(dataset, labels):\n",
    "  items = random.sample(range(len(labels)), 8)\n",
    "  for i, item in enumerate(items):\n",
    "    plt.subplot(2, 4, i+1)\n",
    "    plt.axis('off')\n",
    "    plt.title(pretty_labels[labels[item]])\n",
    "    plt.imshow(dataset[item])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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cR6NcnURZm8VdrSPWM2jbPlONEm94t5nTWmyLIgXh4x5ZP7wfPioOBlaw4Rqf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\nSIeG56iJ8PNIezjwIDrYw1r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WZFDlcjLQNJd0vk761BaT3g7J1PCDRNASyE0N/7aO3FGh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5AAAY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\nihRvEf4/nlJ7kFy0bgAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1169efe50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_sample_dataset(train_dataset, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gNEJvsJdaYfFXGpltn8pSsEV5YAAGB0EeU1nbSjP/58MszGbZWzXAC6dsmF+i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\nH0L8rBJG65AhWesckPaUBaXB/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gkvKHG7ukogerBvwRd+FMmxFmUjxs1/Vd6NUY+aDKiz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\nGG8QxqQ9xhhjjPEGYUzaY4wxxhhvEP4nOVbbyDpWseEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116a12990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_sample_dataset(valid_dataset, valid_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7zIpxmsCA6YFLcPrqkeQ7DPR5caL5LqNt5Sfu8oDVwIQoJQgKAmNG2i8bkpJF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\n4v8DZrHMkeeUa2MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1127ddbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_sample_dataset(test_dataset, test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tIQJaJuwg5Hw"
   },
   "source": [
    "Finally, let's save the data for later reuse:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "QiR_rETzem6C"
   },
   "outputs": [],
   "source": [
    "pickle_file = 'notMNIST.pickle'\n",
    "\n",
    "try:\n",
    "  f = open(pickle_file, 'wb')\n",
    "  save = {\n",
    "    'train_dataset': train_dataset,\n",
    "    'train_labels': train_labels,\n",
    "    'valid_dataset': valid_dataset,\n",
    "    'valid_labels': valid_labels,\n",
    "    'test_dataset': test_dataset,\n",
    "    'test_labels': test_labels,\n",
    "    }\n",
    "  pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n",
    "  f.close()\n",
    "except Exception as e:\n",
    "  print('Unable to save data to', pickle_file, ':', e)\n",
    "  raise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 413065,
     "status": "ok",
     "timestamp": 1444485899688,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "hQbLjrW_iT39",
    "outputId": "b440efc6-5ee1-4cbc-d02d-93db44ebd956"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compressed pickle size: 690800441\n"
     ]
    }
   ],
   "source": [
    "statinfo = os.stat(pickle_file)\n",
    "print('Compressed pickle size:', statinfo.st_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gE_cRAQB33lk"
   },
   "source": [
    "---\n",
    "Problem 5\n",
    "---------\n",
    "\n",
    "By construction, this dataset might contain a lot of overlapping samples, including training data that's also contained in the validation and test set! Overlap between training and test can skew the results if you expect to use your model in an environment where there is never an overlap, but are actually ok if you expect to see training samples recur when you use it.\n",
    "Measure how much overlap there is between training, validation and test samples.\n",
    "\n",
    "Optional questions:\n",
    "- What about near duplicates between datasets? (images that are almost identical)\n",
    "- Create a sanitized validation and test set, and compare your accuracy on those in subsequent assignments.\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part, I will explore the datasets and understand better the overlap cases. There are overlaps, but there are also duplicates in the same dataset! Processing time is also critical. I will first use nested loops and matrix comparison, which is slow and then use hash function to accelerate and process the whole dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def display_overlap(overlap, source_dataset, target_dataset):\n",
    "  item = random.choice(overlap.keys())\n",
    "  imgs = np.concatenate(([source_dataset[item]], target_dataset[overlap[item][0:7]]))\n",
    "  plt.suptitle(item)\n",
    "  for i, img in enumerate(imgs):\n",
    "    plt.subplot(2, 4, i+1)\n",
    "    plt.axis('off')\n",
    "    plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_overlap(dataset_1, dataset_2):\n",
    "  overlap = {}\n",
    "  for i, img_1 in enumerate(dataset_1):\n",
    "    for j, img_2 in enumerate(dataset_2):     \n",
    "      if np.array_equal(img_1, img_2):\n",
    "        if not i in overlap.keys():\n",
    "          overlap[i] = []\n",
    "        overlap[i].append(j)\n",
    "  return overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3min 27s, sys: 198 ms, total: 3min 28s\n",
      "Wall time: 3min 28s\n"
     ]
    }
   ],
   "source": [
    "%time overlap_test_train = extract_overlap(test_dataset[:200], train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of overlaps: 24\n"
     ]
    },
    {
     "data": {
      "image/png": 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0mTDNMg26k+de3ldt2wRBlSBYxrMsKG6QH/PJh+DUaI1+03UCnULEtm0MrlD27DORrKno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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112dd9590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Number of overlaps:', len(overlap_test_train.keys()))\n",
    "display_overlap(overlap_test_train, test_dataset[:200], train_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``display_overlap`` function above display one of the duplicate, the first element is from the first dataset, and the next ones are from the dataset used for the comparison.\n",
    "\n",
    "Now that exact duplicates have been found, let's look for near duplicates. How to define near identical images? That's a tricky question. My first thought has been to use the ``allclose`` numpy matrix comparison. This is too restrictive, since two images can vary by one pyxel, and still be very similar even if the variation on the pyxel is large. A better solution involves some kind of average. \n",
    "\n",
    "To keep is simple and still relevant, I will use a Manhattan norm (sum of absolute values) of the difference matrix. Since the images of the dataset have all the same size, I will not normalize the norm value. Note that it is pyxel by pyxel comparison, and therefore it will not scale to the whole dataset, but it will help to understand image similarities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "MAX_MANHATTAN_NORM = 10\n",
    "\n",
    "def extract_overlap_near(dataset_1, dataset_2):\n",
    "  overlap = {}\n",
    "  for i, img_1 in enumerate(dataset_1):\n",
    "    for j, img_2 in enumerate(dataset_2):\n",
    "      diff = img_1 - img_2\n",
    "      m_norm = np.sum(np.abs(diff))\n",
    "      if m_norm < MAX_MANHATTAN_NORM:\n",
    "        if not i in overlap.keys():\n",
    "          overlap[i] = []\n",
    "        overlap[i].append(j)\n",
    "  return overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5min 1s, sys: 172 ms, total: 5min 1s\n",
      "Wall time: 5min 1s\n"
     ]
    }
   ],
   "source": [
    "%time overlap_test_train_near = extract_overlap_near(test_dataset[:200], train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of near overlaps: 53\n"
     ]
    },
    {
     "data": {
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kWoaf9cJbNA6jTKqnjJR+xrCVQsTspEWdVrkxnzll4qlJM3kEx9Mct2ao9Wxd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\nsfjw4ePY4ROLDx8+jh0+sfjw4ePY8b8fdUBYpnZtHwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104dd85d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Number of near overlaps:', len(overlap_test_train_near.keys()))\n",
    "display_overlap(overlap_test_train_near, test_dataset[:200], train_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The techniques above work well, but the performance is very low and the methods are poorly scalable to the full dataset. Let's try to improve the performance. Let's take some reference times on a small dataset.\n",
    "\n",
    "Here are some ideas:\n",
    "+ stop a the first occurence\n",
    "+ nympy function ``where`` in diff dataset\n",
    "+ hash comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_overlap_stop(dataset_1, dataset_2):\n",
    "  overlap = {}\n",
    "  for i, img_1 in enumerate(dataset_1):\n",
    "    for j, img_2 in enumerate(dataset_2):     \n",
    "      if np.array_equal(img_1, img_2):\n",
    "        overlap[i] = [j]\n",
    "        break\n",
    "  return overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3min 28s, sys: 430 ms, total: 3min 29s\n",
      "Wall time: 3min 29s\n"
     ]
    }
   ],
   "source": [
    "%time overlap_test_train = extract_overlap_stop(test_dataset[:200], train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of overlaps: 24\n"
     ]
    },
    {
     "data": {
      "image/png": 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gpYuxaaBIhjOE+9LW3hj2SOuiKBAKQTAFEzGY8cOYBTsF2F2BXgv6hSN5c/de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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11282aed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Number of overlaps:', len(overlap_test_train.keys()))\n",
    "display_overlap(overlap_test_train, test_dataset[:200], train_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is a faster, and only one duplicate from the second dataset is displayed. This is still not scalable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "MAX_MANHATTAN_NORM = 10\n",
    "\n",
    "def extract_overlap_where(dataset_1, dataset_2):\n",
    "  overlap = {}\n",
    "  for i, img_1 in enumerate(dataset_1):\n",
    "    diff = dataset_2 - img_1\n",
    "    norm = np.sum(np.abs(diff), axis=1)\n",
    "    duplicates = np.where(norm < MAX_MANHATTAN_NORM)\n",
    "    if len(duplicates[0]):\n",
    "      overlap[i] = duplicates[0]\n",
    "  return overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 21s, sys: 1min 14s, total: 2min 36s\n",
      "Wall time: 2min 36s\n"
     ]
    }
   ],
   "source": [
    "test_flat = test_dataset.reshape(test_dataset.shape[0], 28 * 28)\n",
    "train_flat = train_dataset.reshape(train_dataset.shape[0], 28 * 28)\n",
    "%time overlap_test_train = extract_overlap_where(test_flat[:200], train_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of overlaps: 53\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMoAAACKCAYAAADrPdXEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112ad6d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Number of overlaps:', len(overlap_test_train.keys()))\n",
    "display_overlap(overlap_test_train, test_dataset[:200], train_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The built-in numpy function provides some improvement either, but this algorithm is still not scalable to the dataset to its full extend.\n",
    "\n",
    "To make it work at scale, the best option is to use a hash function. To find exact duplicates, the hash functions used for the cryptography will work just fine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_overlap_hash(dataset_1, dataset_2):\n",
    "  dataset_hash_1 = [hashlib.sha256(img).hexdigest() for img in dataset_1]\n",
    "  dataset_hash_2 = [hashlib.sha256(img).hexdigest() for img in dataset_2]\n",
    "  overlap = {}\n",
    "  for i, hash1 in enumerate(dataset_hash_1):\n",
    "    for j, hash2 in enumerate(dataset_hash_2):\n",
    "      if hash1 == hash2:\n",
    "        if not i in overlap.keys():\n",
    "          overlap[i] = []\n",
    "        overlap[i].append(j) ## use np.where\n",
    "  return overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.3 s, sys: 62.6 ms, total: 8.37 s\n",
      "Wall time: 8.33 s\n"
     ]
    }
   ],
   "source": [
    "%time overlap_test_train = extract_overlap_hash(test_dataset[:200], train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of overlaps: 24\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEECAYAAADj+mWwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11679ad10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Number of overlaps:', len(overlap_test_train.keys()))\n",
    "display_overlap(overlap_test_train, test_dataset[:200], train_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More overlapping values could be found, this is due to the hash collisions. Several images can have the same hash but are actually different differents. This is not noticed here, and even if it happens, this is acceptable. All duplicates will be removed for sure.\n",
    "\n",
    "We can make the processing a but faster by using the built-in numpy ``where``function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def extract_overlap_hash_where(dataset_1, dataset_2):\n",
    "  dataset_hash_1 = np.array([hashlib.sha256(img).hexdigest() for img in dataset_1])\n",
    "  dataset_hash_2 = np.array([hashlib.sha256(img).hexdigest() for img in dataset_2])\n",
    "  overlap = {}\n",
    "  for i, hash1 in enumerate(dataset_hash_1):\n",
    "    duplicates = np.where(dataset_hash_2 == hash1)\n",
    "    if len(duplicates[0]):\n",
    "      overlap[i] = duplicates[0]\n",
    "  return overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.31 s, sys: 12.2 ms, total: 2.33 s\n",
      "Wall time: 2.33 s\n"
     ]
    }
   ],
   "source": [
    "%time overlap_test_train = extract_overlap_hash_where(test_dataset[:200], train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of overlaps: 24\n"
     ]
    },
    {
     "data": {
      "image/png": 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PJ7t/katTqLCsvN2fmzbsNKBaZMqrc8WzuD0H+zXYt0HvNn5BlhyA84BWDY6e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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112d65290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Number of overlaps:', len(overlap_test_train.keys()))\n",
    "display_overlap(overlap_test_train, test_dataset[:200], train_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From my perspective near duplicates should also be removed in the sanitized datasets. My assumption is that \"near\" duplicates are very very close (sometimes just there is a one pyxel border of difference), and penalyze the training the same way the true duplicates do.\n",
    "\n",
    "That's being said, finding near duplicates with a hash function is not obvious. There are techniques for that, like \"locally sensitive hashing\", \"perceptual hashing\" or \"difference hashing\". There even are Python library available. Unfortunatly I did not have time to try them. The sanitized dataset generated below are based on true duplicates found with a cryptography hash function.\n",
    "\n",
    "For sanitizing the dataset, I change the function above by returning the clean dataset directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def sanetize(dataset_1, dataset_2, labels_1):\n",
    "  dataset_hash_1 = np.array([hashlib.sha256(img).hexdigest() for img in dataset_1])\n",
    "  dataset_hash_2 = np.array([hashlib.sha256(img).hexdigest() for img in dataset_2])\n",
    "  overlap = [] # list of indexes\n",
    "  for i, hash1 in enumerate(dataset_hash_1):\n",
    "    duplicates = np.where(dataset_hash_2 == hash1)\n",
    "    if len(duplicates[0]):\n",
    "      overlap.append(i) \n",
    "  return np.delete(dataset_1, overlap, 0), np.delete(labels_1, overlap, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.63 s, sys: 21.8 ms, total: 2.65 s\n",
      "Wall time: 2.66 s\n",
      "Overlapping images removed:  24\n"
     ]
    }
   ],
   "source": [
    "%time test_dataset_sanit, test_labels_sanit = sanetize(test_dataset[:200], train_dataset, test_labels[:200])\n",
    "print('Overlapping images removed: ', len(test_dataset[:200]) - len(test_dataset_sanit))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same value is found, so we can now sanetize the test and the train datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 22.9 s, sys: 112 ms, total: 23.1 s\n",
      "Wall time: 23.1 s\n",
      "Overlapping images removed:  1324\n"
     ]
    }
   ],
   "source": [
    "%time test_dataset_sanit, test_labels_sanit = sanetize(test_dataset, train_dataset, test_labels)\n",
    "print('Overlapping images removed: ', len(test_dataset) - len(test_dataset_sanit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 22.9 s, sys: 115 ms, total: 23 s\n",
      "Wall time: 23 s\n",
      "Overlapping images removed:  1067\n"
     ]
    }
   ],
   "source": [
    "%time valid_dataset_sanit, valid_labels_sanit = sanetize(valid_dataset, train_dataset, valid_labels)\n",
    "print('Overlapping images removed: ', len(valid_dataset) - len(valid_dataset_sanit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pickle_file_sanit = 'notMNIST_sanit.pickle'\n",
    "\n",
    "try:\n",
    "  f = open(pickle_file_sanit, 'wb')\n",
    "  save = {\n",
    "    'train_dataset': train_dataset,\n",
    "    'train_labels': train_labels,\n",
    "    'valid_dataset': valid_dataset_sanit,\n",
    "    'valid_labels': valid_labels_sanit,\n",
    "    'test_dataset': test_dataset_sanit,\n",
    "    'test_labels': test_labels_sanit,\n",
    "    }\n",
    "  pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n",
    "  f.close()\n",
    "except Exception as e:\n",
    "  print('Unable to save data to', pickle_file, ':', e)\n",
    "  raise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compressed pickle size: 683292701\n"
     ]
    }
   ],
   "source": [
    "statinfo = os.stat(pickle_file_sanit)\n",
    "print('Compressed pickle size:', statinfo.st_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since I did not have time to generate clean sanitized datasets, I did not use the datasets generated above in the training of the my NN in the next assignments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L8oww1s4JMQx"
   },
   "source": [
    "---\n",
    "Problem 6\n",
    "---------\n",
    "\n",
    "Let's get an idea of what an off-the-shelf classifier can give you on this data. It's always good to check that there is something to learn, and that it's a problem that is not so trivial that a canned solution solves it.\n",
    "\n",
    "Train a simple model on this data using 50, 100, 1000 and 5000 training samples. Hint: you can use the LogisticRegression model from sklearn.linear_model.\n",
    "\n",
    "Optional question: train an off-the-shelf model on all the data!\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have already used scikit-learn in a previous MOOC. It is a great tool, very easy to use!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "regr = LogisticRegression()\n",
    "X_test = test_dataset.reshape(test_dataset.shape[0], 28 * 28)\n",
    "y_test = test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 46.2 ms, sys: 1.59 ms, total: 47.8 ms\n",
      "Wall time: 47 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.50900000000000001"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_size = 50\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "%time regr.fit(X_train, y_train)\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Bck4ofqibooNQZWsJ+2pQ1pMQnkA7Ne0nOYNfTnQSpYYQCjFNJaZJxgdMLp4t8uZb61RX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\nI7xF+P8B90SeQVw0gCgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116759a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_labels = regr.predict(X_test)\n",
    "disp_sample_dataset(test_dataset, pred_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 114 ms, sys: 1.49 ms, total: 115 ms\n",
      "Wall time: 115 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6966"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_size = 100\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "%time regr.fit(X_train, y_train)\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.82 s, sys: 50.1 ms, total: 2.87 s\n",
      "Wall time: 2.3 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.83330000000000004"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_size = 1000\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "%time regr.fit(X_train, y_train)\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.73599999999999999"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_valid = valid_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_valid = valid_labels[:sample_size]\n",
    "regr.score(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Tah/olGDgtiJv7x0f7YyDOW4TZH2CJ6yHx4O0H6vTjksa0fGkk/b+GalG/sVf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\nQom2QqFQnCH+HwZvWaCdTaI4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1128fe550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_labels = regr.predict(X_valid)\n",
    "disp_sample_dataset(valid_dataset, pred_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 19.9 s, sys: 82.7 ms, total: 20 s\n",
      "Wall time: 20 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.85109999999999997"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_size = 5000\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "%time regr.fit(X_train, y_train)\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To train the model on all the data, we have to use another solver. SAG is the faster one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9min 16s, sys: 1.72 s, total: 9min 18s\n",
      "Wall time: 9min 40s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.89319999999999999"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr2 = LogisticRegression(solver='sag')\n",
    "sample_size = len(train_dataset)\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "%time regr2.fit(X_train, y_train)\n",
    "regr2.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SpmJm2IQTaTg2JHjUt8bDnjKlhEvR0ym6CYIO0UcXrei8ekSR9i4lbXvRY+Oj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HAAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104ddb890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_labels = regr.predict(X_test)\n",
    "disp_sample_dataset(test_dataset, pred_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy may be weak compared to a deep neural net, but as my first character recognition technique, I find it already impressive!"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "default_view": {},
   "name": "1_notmnist.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
